Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations99849
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 MiB
Average record size in memory104.0 B

Variable types

Numeric10
Categorical2
Text1

Alerts

Area_in_hectares is highly overall correlated with Production_in_tonsHigh correlation
Crop_Type is highly overall correlated with rainfallHigh correlation
K is highly overall correlated with N and 1 other fieldsHigh correlation
N is highly overall correlated with K and 1 other fieldsHigh correlation
Production_in_tons is highly overall correlated with Area_in_hectaresHigh correlation
State_Name is highly overall correlated with rainfall and 1 other fieldsHigh correlation
Yield_ton_per_hec is highly overall correlated with K and 1 other fieldsHigh correlation
rainfall is highly overall correlated with Crop_Type and 1 other fieldsHigh correlation
temperature is highly overall correlated with State_NameHigh correlation
Yield_ton_per_hec is highly skewed (γ1 = 247.6052597)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
Production_in_tons has 1412 (1.4%) zerosZeros
Yield_ton_per_hec has 1412 (1.4%) zerosZeros

Reproduction

Analysis started2025-11-16 15:22:48.829787
Analysis finished2025-11-16 15:23:02.886816
Duration14.06 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct99849
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49924
Minimum0
Maximum99848
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:02.978833image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4992.4
Q124962
median49924
Q374886
95-th percentile94855.6
Maximum99848
Range99848
Interquartile range (IQR)49924

Descriptive statistics

Standard deviation28824.068
Coefficient of variation (CV)0.57735894
Kurtosis-1.2
Mean49924
Median Absolute Deviation (MAD)24962
Skewness2.1412801 × 10-16
Sum4.9848615 × 109
Variance8.3082689 × 108
MonotonicityStrictly increasing
2025-11-16T20:53:03.098908image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
Other values (99839)99839
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
998481
< 0.1%
998471
< 0.1%
998461
< 0.1%
998451
< 0.1%
998441
< 0.1%
998431
< 0.1%
998421
< 0.1%
998411
< 0.1%
998401
< 0.1%
998391
< 0.1%

State_Name
Categorical

High correlation 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size780.2 KiB
uttar pradesh
12598 
madhya pradesh
9299 
karnataka
9224 
bihar
8608 
odisha
6244 
Other values (28)
53876 

Length

Max length27
Median length16
Mean length9.7855362
Min length3

Characters and Unicode

Total characters977076
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowandhra pradesh
2nd rowandhra pradesh
3rd rowandhra pradesh
4th rowandhra pradesh
5th rowandhra pradesh

Common Values

ValueCountFrequency (%)
uttar pradesh12598
12.6%
madhya pradesh9299
 
9.3%
karnataka9224
 
9.2%
bihar8608
 
8.6%
odisha6244
 
6.3%
tamil nadu6147
 
6.2%
rajasthan5600
 
5.6%
assam5525
 
5.5%
maharashtra4243
 
4.2%
andhra pradesh3802
 
3.8%
Other values (23)28559
28.6%

Length

2025-11-16T20:53:03.200307image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh28036
20.0%
uttar12598
 
9.0%
madhya9299
 
6.6%
karnataka9224
 
6.6%
bihar8608
 
6.1%
odisha6244
 
4.5%
tamil6147
 
4.4%
nadu6147
 
4.4%
rajasthan5600
 
4.0%
assam5525
 
3.9%
Other values (32)42593
30.4%

Most occurring characters

ValueCountFrequency (%)
a236572
24.2%
r94185
 
9.6%
h91995
 
9.4%
t69867
 
7.2%
s63524
 
6.5%
d59375
 
6.1%
n45183
 
4.6%
e42054
 
4.3%
40172
 
4.1%
m32436
 
3.3%
Other values (14)201713
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)977076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a236572
24.2%
r94185
 
9.6%
h91995
 
9.4%
t69867
 
7.2%
s63524
 
6.5%
d59375
 
6.1%
n45183
 
4.6%
e42054
 
4.3%
40172
 
4.1%
m32436
 
3.3%
Other values (14)201713
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)977076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a236572
24.2%
r94185
 
9.6%
h91995
 
9.4%
t69867
 
7.2%
s63524
 
6.5%
d59375
 
6.1%
n45183
 
4.6%
e42054
 
4.3%
40172
 
4.1%
m32436
 
3.3%
Other values (14)201713
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)977076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a236572
24.2%
r94185
 
9.6%
h91995
 
9.4%
t69867
 
7.2%
s63524
 
6.5%
d59375
 
6.1%
n45183
 
4.6%
e42054
 
4.3%
40172
 
4.1%
m32436
 
3.3%
Other values (14)201713
20.6%

Crop_Type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size780.2 KiB
kharif
38758 
rabi
27566 
whole year
26448 
summer
7077 

Length

Max length10
Median length6
Mean length6.5073661
Min length4

Characters and Unicode

Total characters649754
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkharif
2nd rowkharif
3rd rowkharif
4th rowkharif
5th rowkharif

Common Values

ValueCountFrequency (%)
kharif38758
38.8%
rabi27566
27.6%
whole year26448
26.5%
summer7077
 
7.1%

Length

2025-11-16T20:53:03.280847image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T20:53:03.373345image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
ValueCountFrequency (%)
kharif38758
30.7%
rabi27566
21.8%
whole26448
20.9%
year26448
20.9%
summer7077
 
5.6%

Most occurring characters

ValueCountFrequency (%)
r99849
15.4%
a92772
14.3%
i66324
10.2%
h65206
10.0%
e59973
9.2%
k38758
 
6.0%
f38758
 
6.0%
b27566
 
4.2%
w26448
 
4.1%
o26448
 
4.1%
Other values (6)107652
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)649754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r99849
15.4%
a92772
14.3%
i66324
10.2%
h65206
10.0%
e59973
9.2%
k38758
 
6.0%
f38758
 
6.0%
b27566
 
4.2%
w26448
 
4.1%
o26448
 
4.1%
Other values (6)107652
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)649754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r99849
15.4%
a92772
14.3%
i66324
10.2%
h65206
10.0%
e59973
9.2%
k38758
 
6.0%
f38758
 
6.0%
b27566
 
4.2%
w26448
 
4.1%
o26448
 
4.1%
Other values (6)107652
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)649754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r99849
15.4%
a92772
14.3%
i66324
10.2%
h65206
10.0%
e59973
9.2%
k38758
 
6.0%
f38758
 
6.0%
b27566
 
4.2%
w26448
 
4.1%
o26448
 
4.1%
Other values (6)107652
16.6%

Crop
Text

Distinct53
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:03.538471image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1664213
Min length4

Characters and Unicode

Total characters615711
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcotton
2nd rowhorsegram
3rd rowjowar
4th rowmaize
5th rowmoong
ValueCountFrequency (%)
rice11430
 
11.4%
maize9857
 
9.9%
moong6855
 
6.9%
sesamum6291
 
6.3%
wheat6225
 
6.2%
rapeseed5413
 
5.4%
jowar5369
 
5.4%
potato5324
 
5.3%
onion5164
 
5.2%
sunflower3682
 
3.7%
Other values (43)34239
34.3%
2025-11-16T20:53:03.802079image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e78047
12.7%
a76012
12.3%
o66108
10.7%
r52951
 
8.6%
t39644
 
6.4%
i38605
 
6.3%
n36534
 
5.9%
m36051
 
5.9%
s31399
 
5.1%
c25997
 
4.2%
Other values (13)134363
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)615711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e78047
12.7%
a76012
12.3%
o66108
10.7%
r52951
 
8.6%
t39644
 
6.4%
i38605
 
6.3%
n36534
 
5.9%
m36051
 
5.9%
s31399
 
5.1%
c25997
 
4.2%
Other values (13)134363
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)615711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e78047
12.7%
a76012
12.3%
o66108
10.7%
r52951
 
8.6%
t39644
 
6.4%
i38605
 
6.3%
n36534
 
5.9%
m36051
 
5.9%
s31399
 
5.1%
c25997
 
4.2%
Other values (13)134363
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)615711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e78047
12.7%
a76012
12.3%
o66108
10.7%
r52951
 
8.6%
t39644
 
6.4%
i38605
 
6.3%
n36534
 
5.9%
m36051
 
5.9%
s31399
 
5.1%
c25997
 
4.2%
Other values (13)134363
21.8%

N
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.816823
Minimum10
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:03.875823image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q150
median75
Q380
95-th percentile180
Maximum180
Range170
Interquartile range (IQR)30

Descriptive statistics

Standard deviation39.571469
Coefficient of variation (CV)0.56678988
Kurtosis1.0239867
Mean69.816823
Median Absolute Deviation (MAD)25
Skewness0.91200642
Sum6971140
Variance1565.9012
MonotonicityNot monotonic
2025-11-16T20:53:03.979222image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
8028355
28.4%
5015643
15.7%
2012571
12.6%
1208335
 
8.3%
606392
 
6.4%
306291
 
6.3%
1805324
 
5.3%
1004666
 
4.7%
703871
 
3.9%
902920
 
2.9%
Other values (5)5481
 
5.5%
ValueCountFrequency (%)
102253
 
2.3%
2012571
12.6%
252607
 
2.6%
306291
 
6.3%
40177
 
0.2%
5015643
15.7%
606392
 
6.4%
703871
 
3.9%
75327
 
0.3%
8028355
28.4%
ValueCountFrequency (%)
1805324
 
5.3%
160117
 
0.1%
1208335
 
8.3%
1004666
 
4.7%
902920
 
2.9%
8028355
28.4%
75327
 
0.3%
703871
 
3.9%
606392
 
6.4%
5015643
15.7%

P
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.593656
Minimum10
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:04.082673image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q140
median40
Q360
95-th percentile60
Maximum125
Range115
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.056508
Coefficient of variation (CV)0.36199048
Kurtosis0.69833047
Mean41.593656
Median Absolute Deviation (MAD)0
Skewness0.12088189
Sum4153085
Variance226.69843
MonotonicityNot monotonic
2025-11-16T20:53:04.191814image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4051650
51.7%
6022542
22.6%
156573
 
6.6%
306225
 
6.2%
205561
 
5.6%
102679
 
2.7%
752646
 
2.7%
501563
 
1.6%
65125
 
0.1%
125107
 
0.1%
Other values (3)178
 
0.2%
ValueCountFrequency (%)
102679
 
2.7%
156573
 
6.6%
205561
 
5.6%
306225
 
6.2%
4051650
51.7%
4528
 
< 0.1%
501563
 
1.6%
6022542
22.6%
65125
 
0.1%
70105
 
0.1%
ValueCountFrequency (%)
125107
 
0.1%
10045
 
< 0.1%
752646
 
2.7%
70105
 
0.1%
65125
 
0.1%
6022542
22.6%
501563
 
1.6%
4528
 
< 0.1%
4051650
51.7%
306225
 
6.2%

K
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.037827
Minimum10
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:04.294404image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q120
median30
Q350
95-th percentile100
Maximum200
Range190
Interquartile range (IQR)30

Descriptive statistics

Standard deviation28.430263
Coefficient of variation (CV)0.67630192
Kurtosis2.9232369
Mean42.037827
Median Absolute Deviation (MAD)10
Skewness1.7543017
Sum4197435
Variance808.27987
MonotonicityNot monotonic
2025-11-16T20:53:04.413551image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2035803
35.9%
4018380
18.4%
3016599
16.6%
905608
 
5.6%
655164
 
5.2%
504752
 
4.8%
453139
 
3.1%
1203016
 
3.0%
602898
 
2.9%
1002576
 
2.6%
Other values (6)1914
 
1.9%
ValueCountFrequency (%)
10219
 
0.2%
2035803
35.9%
3016599
16.6%
4018380
18.4%
453139
 
3.1%
504752
 
4.8%
602898
 
2.9%
655164
 
5.2%
70125
 
0.1%
8572
 
0.1%
ValueCountFrequency (%)
200107
 
0.1%
150237
 
0.2%
1401154
 
1.2%
1203016
3.0%
1002576
2.6%
905608
5.6%
8572
 
0.1%
70125
 
0.1%
655164
5.2%
602898
2.9%

pH
Real number (ℝ)

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6436243
Minimum3.82
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:04.549815image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum3.82
5-th percentile4.92
Q15.36
median5.54
Q35.96
95-th percentile6.6
Maximum7
Range3.18
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.50528257
Coefficient of variation (CV)0.089531576
Kurtosis-0.078161959
Mean5.6436243
Median Absolute Deviation (MAD)0.2
Skewness0.57134731
Sum563510.24
Variance0.25531048
MonotonicityNot monotonic
2025-11-16T20:53:04.683846image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.362922
 
2.9%
5.422875
 
2.9%
5.42859
 
2.9%
5.382856
 
2.9%
5.322834
 
2.8%
5.62815
 
2.8%
5.622803
 
2.8%
5.462798
 
2.8%
5.522798
 
2.8%
5.442796
 
2.8%
Other values (91)71493
71.6%
ValueCountFrequency (%)
3.8212
< 0.1%
3.847
< 0.1%
3.8614
< 0.1%
3.8811
< 0.1%
3.913
< 0.1%
3.9216
< 0.1%
3.9417
< 0.1%
3.9612
< 0.1%
3.9814
< 0.1%
48
< 0.1%
ValueCountFrequency (%)
7555
0.6%
6.9550
0.6%
6.8579
0.6%
6.7543
0.5%
6.68636
0.6%
6.66644
0.6%
6.64664
0.7%
6.62613
0.6%
6.61196
1.2%
6.58658
0.7%

rainfall
Real number (ℝ)

High correlation 

Distinct111
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.15108
Minimum3.274569
Maximum3322.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:04.827371image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum3.274569
5-th percentile41.3
Q1157.31
median579.75
Q31110.78
95-th percentile1712.66
Maximum3322.06
Range3318.7854
Interquartile range (IQR)953.47

Descriptive statistics

Standard deviation604.70155
Coefficient of variation (CV)0.86244116
Kurtosis1.5368943
Mean701.15108
Median Absolute Deviation (MAD)446.89
Skewness1.1449654
Sum70009235
Variance365663.97
MonotonicityNot monotonic
2025-11-16T20:53:04.964170image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
579.754880
 
4.9%
75.324814
 
4.8%
1111.683967
 
4.0%
884.53966
 
4.0%
1011.493515
 
3.5%
1246.7153169
 
3.2%
840.462816
 
2.8%
87.22627
 
2.6%
510.052562
 
2.6%
607.482549
 
2.6%
Other values (101)64984
65.1%
ValueCountFrequency (%)
3.27456950
 
0.1%
3.94111
 
0.1%
5.27434
 
< 0.1%
9.62704418
 
< 0.1%
10.26574880
 
0.1%
15.34628
 
0.6%
19.381367
1.4%
34.811707
1.7%
35.21424
 
< 0.1%
37.09235
 
0.2%
ValueCountFrequency (%)
3322.0674
 
0.1%
3041.418
 
< 0.1%
2879.8629
 
< 0.1%
2817.861559
1.6%
2569.52272
 
0.3%
2459.648
 
< 0.1%
2169.322399
2.4%
1997.12341
 
0.3%
1925.6821
 
< 0.1%
1875.6143
 
0.1%

temperature
Real number (ℝ)

High correlation 

Distinct109
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.684154
Minimum1.18
Maximum35.346667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:05.095839image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum1.18
5-th percentile20.312
Q123.106
median27.333333
Q329.266667
95-th percentile34.01
Maximum35.346667
Range34.166667
Interquartile range (IQR)6.1606667

Descriptive statistics

Standard deviation4.8512138
Coefficient of variation (CV)0.1818013
Kurtosis2.3340402
Mean26.684154
Median Absolute Deviation (MAD)3.2833333
Skewness-0.7647918
Sum2664386.1
Variance23.534276
MonotonicityNot monotonic
2025-11-16T20:53:05.219483image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.014880
 
4.9%
22.6764814
 
4.8%
28.648181823967
 
4.0%
27.654545453966
 
4.0%
30.433515
 
3.5%
22.63169
 
3.2%
33.583333332816
 
2.8%
23.1062627
 
2.6%
33.373333332562
 
2.6%
26.366666672549
 
2.6%
Other values (99)64984
65.1%
ValueCountFrequency (%)
1.18170
 
0.2%
4.9272
0.3%
10.38545
0.5%
11.2470
0.5%
12.5139
 
0.1%
14.6582
0.6%
14.7331
0.3%
15.5167
 
0.2%
15.618181828
 
< 0.1%
15.852246
0.2%
ValueCountFrequency (%)
35.34666667945
 
0.9%
34.923333331188
 
1.2%
34.73635
 
0.6%
34.666666671707
 
1.7%
34.014880
4.9%
33.76333333111
 
0.1%
33.583333332816
2.8%
33.373333332562
2.6%
30.616666671344
 
1.3%
30.433515
3.5%

Area_in_hectares
Real number (ℝ)

High correlation 

Distinct26346
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16476.586
Minimum0.58
Maximum726300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:05.365580image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile10
Q1130
median1010
Q38099
95-th percentile98990.6
Maximum726300
Range726299.42
Interquartile range (IQR)7969

Descriptive statistics

Standard deviation43604.268
Coefficient of variation (CV)2.6464384
Kurtosis31.742659
Mean16476.586
Median Absolute Deviation (MAD)990
Skewness4.7569317
Sum1.6451706 × 109
Variance1.9013322 × 109
MonotonicityNot monotonic
2025-11-16T20:53:05.502052image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2639
 
0.6%
5608
 
0.6%
1598
 
0.6%
3594
 
0.6%
4574
 
0.6%
10533
 
0.5%
6503
 
0.5%
7457
 
0.5%
8451
 
0.5%
20429
 
0.4%
Other values (26336)94463
94.6%
ValueCountFrequency (%)
0.581
 
< 0.1%
1598
0.6%
1.51
 
< 0.1%
1.622
 
< 0.1%
2639
0.6%
2.081
 
< 0.1%
2.091
 
< 0.1%
2.52
 
< 0.1%
2.571
 
< 0.1%
2.781
 
< 0.1%
ValueCountFrequency (%)
7263001
< 0.1%
7129001
< 0.1%
7113001
< 0.1%
6999001
< 0.1%
6875001
< 0.1%
6869001
< 0.1%
6721001
< 0.1%
6576001
< 0.1%
6412001
< 0.1%
6367001
< 0.1%

Production_in_tons
Real number (ℝ)

High correlation  Zeros 

Distinct33217
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37762.912
Minimum0
Maximum3530571
Zeros1412
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:05.629079image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q1162
median1506
Q314395
95-th percentile217680
Maximum3530571
Range3530571
Interquartile range (IQR)14233

Descriptive statistics

Standard deviation122244.67
Coefficient of variation (CV)3.2371622
Kurtosis81.953586
Mean37762.912
Median Absolute Deviation (MAD)1491
Skewness7.2254252
Sum3.770589 × 109
Variance1.494376 × 1010
MonotonicityNot monotonic
2025-11-16T20:53:05.740879image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01412
 
1.4%
1556
 
0.6%
2553
 
0.6%
3497
 
0.5%
10475
 
0.5%
4444
 
0.4%
5434
 
0.4%
6424
 
0.4%
100409
 
0.4%
8387
 
0.4%
Other values (33207)94258
94.4%
ValueCountFrequency (%)
01412
1.4%
0.015
 
< 0.1%
0.133
 
< 0.1%
0.216
 
< 0.1%
0.315
 
< 0.1%
0.311
 
< 0.1%
0.381
 
< 0.1%
0.418
 
< 0.1%
0.520
 
< 0.1%
0.511
 
< 0.1%
ValueCountFrequency (%)
35305711
< 0.1%
34344591
< 0.1%
25895911
< 0.1%
24823951
< 0.1%
24652121
< 0.1%
24481361
< 0.1%
24109631
< 0.1%
23908401
< 0.1%
23563891
< 0.1%
23500431
< 0.1%

Yield_ton_per_hec
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct72860
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9311494
Minimum0
Maximum9801
Zeros1412
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size780.2 KiB
2025-11-16T20:53:05.865026image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.175
Q10.5862069
median1.3292683
Q32.9972882
95-th percentile16.268844
Maximum9801
Range9801
Interquartile range (IQR)2.4110813

Descriptive statistics

Standard deviation33.872242
Coefficient of variation (CV)8.616371
Kurtosis70354.303
Mean3.9311494
Median Absolute Deviation (MAD)0.89829781
Skewness247.60526
Sum392521.34
Variance1147.3288
MonotonicityNot monotonic
2025-11-16T20:53:05.997983image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01412
 
1.4%
11034
 
1.0%
0.5698
 
0.7%
2489
 
0.5%
0.3333333333350
 
0.4%
1.5300
 
0.3%
0.6666666667289
 
0.3%
3264
 
0.3%
0.6251
 
0.3%
0.4242
 
0.2%
Other values (72850)94520
94.7%
ValueCountFrequency (%)
01412
1.4%
0.00051413881751
 
< 0.1%
0.00081321691491
 
< 0.1%
0.001173192551
 
< 0.1%
0.0012277470841
 
< 0.1%
0.0012777326051
 
< 0.1%
0.0012820512821
 
< 0.1%
0.0013774104681
 
< 0.1%
0.0016583747931
 
< 0.1%
0.0016849199661
 
< 0.1%
ValueCountFrequency (%)
98011
< 0.1%
21501
< 0.1%
14941
< 0.1%
1326.6666671
< 0.1%
1142.51
< 0.1%
11271
< 0.1%
11131
< 0.1%
725.251
< 0.1%
3001
< 0.1%
235.55555561
< 0.1%

Interactions

2025-11-16T20:53:01.460623image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:51.701896image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.685602image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.656982image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.678058image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.787880image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.872831image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.307265image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.378515image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.439859image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.569990image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:51.810737image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.779996image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.754938image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.781899image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.896246image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.968539image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.409344image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.482203image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.554078image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.658617image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:51.906371image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.872061image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.858019image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.882070image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.005744image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:57.064329image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.525746image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.588392image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.662689image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.751220image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.000234image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.967156image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.951025image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.999340image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.105754image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:57.155789image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.629886image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.693650image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.764216image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.842305image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.100506image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.073770image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.047762image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.101866image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.215714image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:57.257737image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.732915image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.794966image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.865914image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.935778image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.193230image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.170418image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.150667image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.198947image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.327406image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:57.355901image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.843782image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.896125image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.970891image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:02.033140image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.286363image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.264119image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.271646image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.297125image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.442095image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:57.460748image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.946446image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.993530image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.067495image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:02.140302image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.388255image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.363767image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.380058image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.400910image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.560719image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:57.977328image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.046290image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.103682image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.165070image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:02.237649image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.493352image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.463321image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.485105image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.531648image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.663418image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.091222image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.163563image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.203497image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.265922image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:02.343479image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:52.593828image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:53.561966image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:54.583685image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:55.677800image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:56.767381image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:58.203908image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:52:59.278198image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:00.325134image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
2025-11-16T20:53:01.365628image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/

Correlations

2025-11-16T20:53:06.111397image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Area_in_hectaresCrop_TypeKNPProduction_in_tonsState_NameUnnamed: 0Yield_ton_per_hecpHrainfalltemperature
Area_in_hectares1.0000.107-0.1320.070-0.0850.8950.096-0.0250.0170.038-0.140-0.051
Crop_Type0.1071.0000.4290.3980.3980.0560.2840.0810.0040.3280.5840.450
K-0.1320.4291.0000.5240.2190.1080.1440.0010.523-0.0970.262-0.052
N0.0700.3980.5241.0000.2630.3270.1510.0070.608-0.1460.1120.030
P-0.0850.3980.2190.2631.0000.0670.1490.0060.264-0.2370.131-0.032
Production_in_tons0.8950.0560.1080.3270.0671.0000.126-0.0000.432-0.020-0.096-0.071
State_Name0.0960.2840.1440.1510.1490.1261.0000.1230.0160.0900.6410.560
Unnamed: 0-0.0250.0810.0010.0070.006-0.0000.1231.0000.059-0.003-0.048-0.036
Yield_ton_per_hec0.0170.0040.5230.6080.2640.4320.0160.0591.000-0.1410.046-0.069
pH0.0380.328-0.097-0.146-0.237-0.0200.090-0.003-0.1411.000-0.0040.034
rainfall-0.1400.5840.2620.1120.131-0.0960.641-0.0480.046-0.0041.0000.155
temperature-0.0510.450-0.0520.030-0.032-0.0710.560-0.036-0.0690.0340.1551.000

Missing values

2025-11-16T20:53:02.485635image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-16T20:53:02.679408image/svg+xmlMatplotlib v3.10.7, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0State_NameCrop_TypeCropNPKpHrainfalltemperatureArea_in_hectaresProduction_in_tonsYield_ton_per_hec
00andhra pradeshkharifcotton12040205.46654.3429.2666677300.09400.01.287671
11andhra pradeshkharifhorsegram2060206.18654.3429.2666673300.01000.00.303030
22andhra pradeshkharifjowar8040405.42654.3429.26666710100.010200.01.009901
33andhra pradeshkharifmaize8040205.62654.3429.2666672800.04900.01.750000
44andhra pradeshkharifmoong2040205.68654.3429.2666671300.0500.00.384615
55andhra pradeshkharifragi5040205.64654.3429.2666676700.011800.01.761194
66andhra pradeshkharifrice8040405.54654.3429.26666735600.075400.02.117978
77andhra pradeshkharifsunflower5060305.36654.3429.26666735900.011100.00.309192
88andhra pradeshrabihorsegram2060206.00288.3025.460000600.0200.00.333333
99andhra pradeshrabijowar8040405.50288.3025.46000018800.09400.00.500000
Unnamed: 0State_NameCrop_TypeCropNPKpHrainfalltemperatureArea_in_hectaresProduction_in_tonsYield_ton_per_hec
9983999839west bengalkharifmoong2040205.501166.9428.333333293.0136.00.464164
9984099840west bengalkharifsunflower5060305.621166.9428.33333337.040.01.081081
9984199841west bengalrabimoong2040205.62152.5422.28000052.042.00.807692
9984299842west bengalrabipotato18060904.84152.5422.280000977.015920.016.294780
9984399843west bengalrabirapeseed5040205.12152.5422.280000886.0542.00.611738
9984499844west bengalrabiwheat6030306.70152.5422.2800002013.05152.02.559364
9984599845west bengalsummermaize8040205.68182.5029.200000258.0391.01.515504
9984699846west bengalsummerrice8040405.64182.5029.200000105.0281.02.676190
9984799847west bengalrabirice8040405.42152.5422.280000152676.0261435.01.712352
9984899848west bengalrabisesamum3015306.54152.5422.280000244.095.00.389344